Overview

Dataset statistics

Number of variables15
Number of observations7222
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory846.5 KiB
Average record size in memory120.0 B

Variable types

Numeric15

Alerts

TIME is highly correlated with S and 13 other fieldsHigh correlation
S is highly correlated with TIME and 13 other fieldsHigh correlation
T1 is highly correlated with TIME and 13 other fieldsHigh correlation
T2 is highly correlated with TIME and 13 other fieldsHigh correlation
T3 is highly correlated with TIME and 13 other fieldsHigh correlation
T4 is highly correlated with TIME and 13 other fieldsHigh correlation
T5 is highly correlated with TIME and 13 other fieldsHigh correlation
T6 is highly correlated with TIME and 13 other fieldsHigh correlation
T7 is highly correlated with TIME and 13 other fieldsHigh correlation
T8 is highly correlated with TIME and 13 other fieldsHigh correlation
T9 is highly correlated with TIME and 13 other fieldsHigh correlation
T10 is highly correlated with TIME and 13 other fieldsHigh correlation
T11 is highly correlated with TIME and 13 other fieldsHigh correlation
T12 is highly correlated with TIME and 13 other fieldsHigh correlation
Z is highly correlated with TIME and 13 other fieldsHigh correlation
TIME is uniformly distributed Uniform
TIME has unique values Unique
S has 1231 (17.0%) zeros Zeros

Reproduction

Analysis started2022-11-11 03:27:06.081370
Analysis finished2022-11-11 03:27:19.206018
Duration13.12 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

TIME
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct7222
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.875
Minimum0
Maximum601.75
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.235917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30.0875
Q1150.4375
median300.875
Q3451.3125
95-th percentile571.6625
Maximum601.75
Range601.75
Interquartile range (IQR)300.875

Descriptive statistics

Standard deviation173.7463462
Coefficient of variation (CV)0.5774701993
Kurtosis-1.2
Mean300.875
Median Absolute Deviation (MAD)150.4583333
Skewness0
Sum2172919.25
Variance30187.79282
MonotonicityStrictly increasing
2022-11-11T11:27:19.298705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
404.33333331
 
< 0.1%
401.83333331
 
< 0.1%
401.751
 
< 0.1%
401.66666671
 
< 0.1%
401.58333331
 
< 0.1%
401.51
 
< 0.1%
401.41666671
 
< 0.1%
401.33333331
 
< 0.1%
401.251
 
< 0.1%
Other values (7212)7212
99.9%
ValueCountFrequency (%)
01
< 0.1%
0.083333333331
< 0.1%
0.16666666671
< 0.1%
0.251
< 0.1%
0.33333333331
< 0.1%
0.41666666671
< 0.1%
0.51
< 0.1%
0.58333333331
< 0.1%
0.66666666671
< 0.1%
0.751
< 0.1%
ValueCountFrequency (%)
601.751
< 0.1%
601.66666671
< 0.1%
601.58333331
< 0.1%
601.51
< 0.1%
601.41666671
< 0.1%
601.33333331
< 0.1%
601.251
< 0.1%
601.16666671
< 0.1%
601.08333331
< 0.1%
6011
< 0.1%

S
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10890.40917
Minimum0
Maximum20001
Zeros1231
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.358503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18000
median12001
Q315999
95-th percentile19999
Maximum20001
Range20001
Interquartile range (IQR)7999

Descriptive statistics

Standard deviation5868.932755
Coefficient of variation (CV)0.5389083794
Kurtosis-0.4219593385
Mean10890.40917
Median Absolute Deviation (MAD)3998
Skewness-0.6724084407
Sum78650535
Variance34444371.68
MonotonicityNot monotonic
2022-11-11T11:27:19.404350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
120011323
18.3%
01231
17.0%
15999988
13.7%
14000887
12.3%
9998765
10.6%
8000692
9.6%
18000436
 
6.0%
20001286
 
4.0%
7999256
 
3.5%
13999229
 
3.2%
Other values (5)129
 
1.8%
ValueCountFrequency (%)
01231
17.0%
7999256
 
3.5%
8000692
9.6%
9998765
10.6%
112651
 
< 0.1%
119551
 
< 0.1%
120011323
18.3%
13999229
 
3.2%
14000887
12.3%
148431
 
< 0.1%
ValueCountFrequency (%)
20001286
 
4.0%
19999118
 
1.6%
18000436
 
6.0%
179988
 
0.1%
15999988
13.7%
148431
 
< 0.1%
14000887
12.3%
13999229
 
3.2%
120011323
18.3%
119551
 
< 0.1%

T1
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.72903628
Minimum22.3
Maximum25.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.523946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.9025
Q123.1
median23.5
Q324.2
95-th percentile25.2
Maximum25.5
Range3.2
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.6932260817
Coefficient of variation (CV)0.02921425352
Kurtosis-0.4047933005
Mean23.72903628
Median Absolute Deviation (MAD)0.5
Skewness0.655761621
Sum171371.1
Variance0.4805624003
MonotonicityNot monotonic
2022-11-11T11:27:19.578933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.11016
14.1%
24.1933
12.9%
23.4823
11.4%
23776
10.7%
23.6568
7.9%
24.7389
 
5.4%
24.5374
 
5.2%
24.2350
 
4.8%
24.3314
 
4.3%
23.2264
 
3.7%
Other values (55)1415
19.6%
ValueCountFrequency (%)
22.329
0.4%
22.351
 
< 0.1%
22.42
 
< 0.1%
22.451
 
< 0.1%
22.53
 
< 0.1%
22.551
 
< 0.1%
22.631
0.4%
22.651
 
< 0.1%
22.731
0.4%
22.751
 
< 0.1%
ValueCountFrequency (%)
25.515
 
0.2%
25.452
 
< 0.1%
25.4261
3.6%
25.352
 
< 0.1%
25.369
 
1.0%
25.252
 
< 0.1%
25.216
 
0.2%
25.152
 
< 0.1%
25.121
 
0.3%
25.052
 
< 0.1%

T2
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.52918859
Minimum22.2
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.630896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.2
Q122.3
median22.5
Q322.7
95-th percentile23
Maximum23
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2623720387
Coefficient of variation (CV)0.01164587165
Kurtosis-0.7874499596
Mean22.52918859
Median Absolute Deviation (MAD)0.2
Skewness0.6083845079
Sum162705.8
Variance0.06883908671
MonotonicityNot monotonic
2022-11-11T11:27:19.674748image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
22.41461
20.2%
231165
16.1%
22.21068
14.8%
22.3983
13.6%
22.6973
13.5%
22.5819
11.3%
22.8401
 
5.6%
22.7335
 
4.6%
22.917
 
0.2%
ValueCountFrequency (%)
22.21068
14.8%
22.3983
13.6%
22.41461
20.2%
22.5819
11.3%
22.6973
13.5%
22.7335
 
4.6%
22.8401
 
5.6%
22.917
 
0.2%
231165
16.1%
ValueCountFrequency (%)
231165
16.1%
22.917
 
0.2%
22.8401
 
5.6%
22.7335
 
4.6%
22.6973
13.5%
22.5819
11.3%
22.41461
20.2%
22.3983
13.6%
22.21068
14.8%

T3
Real number (ℝ≥0)

HIGH CORRELATION

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5107311
Minimum22.1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.720613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.1
5-th percentile22.2
Q122.3
median22.4
Q322.7
95-th percentile23
Maximum23
Range0.9
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2644254784
Coefficient of variation (CV)0.01174664107
Kurtosis-0.547477296
Mean22.5107311
Median Absolute Deviation (MAD)0.1
Skewness0.6572493225
Sum162572.5
Variance0.06992083365
MonotonicityNot monotonic
2022-11-11T11:27:19.760422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
22.41517
21.0%
231168
16.2%
22.51102
15.3%
22.3997
13.8%
22.2836
11.6%
22.6656
9.1%
22.7581
 
8.0%
22.1282
 
3.9%
22.868
 
0.9%
22.915
 
0.2%
ValueCountFrequency (%)
22.1282
 
3.9%
22.2836
11.6%
22.3997
13.8%
22.41517
21.0%
22.51102
15.3%
22.6656
9.1%
22.7581
 
8.0%
22.868
 
0.9%
22.915
 
0.2%
231168
16.2%
ValueCountFrequency (%)
231168
16.2%
22.915
 
0.2%
22.868
 
0.9%
22.7581
 
8.0%
22.6656
9.1%
22.51102
15.3%
22.41517
21.0%
22.3997
13.8%
22.2836
11.6%
22.1282
 
3.9%

T4
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.85048463
Minimum22.3
Maximum23.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.802626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.3
5-th percentile22.4
Q122.5
median22.8
Q323.1
95-th percentile23.4
Maximum23.4
Range1.1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.3081753853
Coefficient of variation (CV)0.01348660172
Kurtosis-0.9767457455
Mean22.85048463
Median Absolute Deviation (MAD)0.3
Skewness0.1391415823
Sum165026.2
Variance0.09497206811
MonotonicityNot monotonic
2022-11-11T11:27:19.845484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
22.81335
18.5%
22.51330
18.4%
23.1740
10.2%
23683
9.5%
22.9623
8.6%
23.4591
8.2%
22.6540
7.5%
23.2420
 
5.8%
23.3324
 
4.5%
22.3257
 
3.6%
Other values (2)379
 
5.2%
ValueCountFrequency (%)
22.3257
 
3.6%
22.4225
 
3.1%
22.51330
18.4%
22.6540
7.5%
22.7154
 
2.1%
22.81335
18.5%
22.9623
8.6%
23683
9.5%
23.1740
10.2%
23.2420
 
5.8%
ValueCountFrequency (%)
23.4591
8.2%
23.3324
 
4.5%
23.2420
 
5.8%
23.1740
10.2%
23683
9.5%
22.9623
8.6%
22.81335
18.5%
22.7154
 
2.1%
22.6540
7.5%
22.51330
18.4%

T5
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.74300748
Minimum22.4
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.890333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.4
5-th percentile22.4
Q122.5
median22.6
Q323
95-th percentile23.2
Maximum23.2
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.2688772137
Coefficient of variation (CV)0.0118224124
Kurtosis-1.362065725
Mean22.74300748
Median Absolute Deviation (MAD)0.1
Skewness0.4407897248
Sum164250
Variance0.07229495604
MonotonicityNot monotonic
2022-11-11T11:27:19.932733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
22.52220
30.7%
23.11105
15.3%
22.61055
14.6%
23.2610
 
8.4%
22.9551
 
7.6%
22.4508
 
7.0%
22.7402
 
5.6%
23391
 
5.4%
22.8380
 
5.3%
ValueCountFrequency (%)
22.4508
 
7.0%
22.52220
30.7%
22.61055
14.6%
22.7402
 
5.6%
22.8380
 
5.3%
22.9551
 
7.6%
23391
 
5.4%
23.11105
15.3%
23.2610
 
8.4%
ValueCountFrequency (%)
23.2610
 
8.4%
23.11105
15.3%
23391
 
5.4%
22.9551
 
7.6%
22.8380
 
5.3%
22.7402
 
5.6%
22.61055
14.6%
22.52220
30.7%
22.4508
 
7.0%

T6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.67416228
Minimum22.5
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:19.974992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.5
5-th percentile22.5
Q122.5
median22.6
Q322.9
95-th percentile23
Maximum23
Range0.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.1792360505
Coefficient of variation (CV)0.007904858768
Kurtosis-1.348718984
Mean22.67416228
Median Absolute Deviation (MAD)0.1
Skewness0.479316909
Sum163752.8
Variance0.03212556181
MonotonicityNot monotonic
2022-11-11T11:27:20.018959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
22.52878
39.9%
22.91548
21.4%
22.61133
 
15.7%
22.7708
 
9.8%
23486
 
6.7%
22.8469
 
6.5%
ValueCountFrequency (%)
22.52878
39.9%
22.61133
 
15.7%
22.7708
 
9.8%
22.8469
 
6.5%
22.91548
21.4%
23486
 
6.7%
ValueCountFrequency (%)
23486
 
6.7%
22.91548
21.4%
22.8469
 
6.5%
22.7708
 
9.8%
22.61133
 
15.7%
22.52878
39.9%

T7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.42729161
Minimum22
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.063875image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q122.2
median22.3
Q322.5
95-th percentile23
Maximum23
Range1
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.2816875039
Coefficient of variation (CV)0.01256003216
Kurtosis-0.07747850759
Mean22.42729161
Median Absolute Deviation (MAD)0.1
Skewness0.9398359092
Sum161969.9
Variance0.07934784986
MonotonicityNot monotonic
2022-11-11T11:27:20.110749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
22.31674
23.2%
22.21634
22.6%
22.41017
14.1%
23979
13.6%
22.5683
9.5%
22.6564
 
7.8%
22366
 
5.1%
22.9164
 
2.3%
22.197
 
1.3%
22.831
 
0.4%
ValueCountFrequency (%)
22366
 
5.1%
22.197
 
1.3%
22.21634
22.6%
22.31674
23.2%
22.41017
14.1%
22.5683
9.5%
22.6564
 
7.8%
22.713
 
0.2%
22.831
 
0.4%
22.9164
 
2.3%
ValueCountFrequency (%)
23979
13.6%
22.9164
 
2.3%
22.831
 
0.4%
22.713
 
0.2%
22.6564
 
7.8%
22.5683
9.5%
22.41017
14.1%
22.31674
23.2%
22.21634
22.6%
22.197
 
1.3%

T8
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.5787455
Minimum22.2
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.155607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.2
5-th percentile22.2
Q122.4
median22.5
Q322.8
95-th percentile23
Maximum23
Range0.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.258131149
Coefficient of variation (CV)0.0114324841
Kurtosis-1.177734429
Mean22.5787455
Median Absolute Deviation (MAD)0.2
Skewness0.4196359334
Sum163063.7
Variance0.0666316901
MonotonicityNot monotonic
2022-11-11T11:27:20.198545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
22.41476
20.4%
22.31177
16.3%
231159
16.0%
22.51105
15.3%
22.7613
8.5%
22.8559
 
7.7%
22.9390
 
5.4%
22.6372
 
5.2%
22.2371
 
5.1%
ValueCountFrequency (%)
22.2371
 
5.1%
22.31177
16.3%
22.41476
20.4%
22.51105
15.3%
22.6372
 
5.2%
22.7613
8.5%
22.8559
 
7.7%
22.9390
 
5.4%
231159
16.0%
ValueCountFrequency (%)
231159
16.0%
22.9390
 
5.4%
22.8559
 
7.7%
22.7613
8.5%
22.6372
 
5.2%
22.51105
15.3%
22.41476
20.4%
22.31177
16.3%
22.2371
 
5.1%

T9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct71
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.85947106
Minimum23.1
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.256726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum23.1
5-th percentile23.7
Q124.1
median24.6
Q325.75
95-th percentile26.2
Maximum26.6
Range3.5
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation0.8634524232
Coefficient of variation (CV)0.0347333385
Kurtosis-1.299186912
Mean24.85947106
Median Absolute Deviation (MAD)0.7
Skewness0.1964773323
Sum179535.1
Variance0.7455500872
MonotonicityNot monotonic
2022-11-11T11:27:20.316207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.5514
 
7.1%
25.8430
 
6.0%
24416
 
5.8%
24.6401
 
5.6%
25.9352
 
4.9%
24.1345
 
4.8%
23.8343
 
4.7%
24.7319
 
4.4%
24.2299
 
4.1%
23.9295
 
4.1%
Other values (61)3508
48.6%
ValueCountFrequency (%)
23.137
0.5%
23.151
 
< 0.1%
23.252
0.7%
23.251
 
< 0.1%
23.33
 
< 0.1%
23.351
 
< 0.1%
23.44
 
0.1%
23.451
 
< 0.1%
23.555
0.8%
23.551
 
< 0.1%
ValueCountFrequency (%)
26.629
 
0.4%
26.554
 
0.1%
26.520
 
0.3%
26.454
 
0.1%
26.464
0.9%
26.358
 
0.1%
26.395
1.3%
26.2510
 
0.1%
26.2143
2.0%
26.1514
 
0.2%

T10
Real number (ℝ≥0)

HIGH CORRELATION

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.65497092
Minimum22
Maximum23.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.373178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22.3
Q122.4
median22.6
Q322.9
95-th percentile23.2
Maximum23.4
Range1.4
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.3099594445
Coefficient of variation (CV)0.013681741
Kurtosis-0.7634233595
Mean22.65497092
Median Absolute Deviation (MAD)0.2
Skewness0.484754694
Sum163614.2
Variance0.09607485721
MonotonicityNot monotonic
2022-11-11T11:27:20.416659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
22.51349
18.7%
22.3999
13.8%
22.4968
13.4%
22.6605
8.4%
22.7557
7.7%
23524
 
7.3%
22.9510
 
7.1%
22.8441
 
6.1%
23.1423
 
5.9%
23.2417
 
5.8%
Other values (5)429
 
5.9%
ValueCountFrequency (%)
2245
 
0.6%
22.160
 
0.8%
22.2109
 
1.5%
22.3999
13.8%
22.4968
13.4%
22.51349
18.7%
22.6605
8.4%
22.7557
7.7%
22.8441
 
6.1%
22.9510
 
7.1%
ValueCountFrequency (%)
23.471
 
1.0%
23.3144
 
2.0%
23.2417
 
5.8%
23.1423
 
5.9%
23524
 
7.3%
22.9510
 
7.1%
22.8441
 
6.1%
22.7557
7.7%
22.6605
8.4%
22.51349
18.7%

T11
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.42741623
Minimum21.5
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.467973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.5
5-th percentile21.7
Q121.9
median22.4
Q322.9
95-th percentile23.2
Maximum23.5
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5163644693
Coefficient of variation (CV)0.02302380551
Kurtosis-1.297592318
Mean22.42741623
Median Absolute Deviation (MAD)0.5
Skewness0.1311807133
Sum161970.8
Variance0.2666322652
MonotonicityNot monotonic
2022-11-11T11:27:20.594651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
21.8725
 
10.0%
21.9596
 
8.3%
22572
 
7.9%
23.1559
 
7.7%
22.7514
 
7.1%
22.2473
 
6.5%
22.8457
 
6.3%
22.9379
 
5.2%
23375
 
5.2%
22.1330
 
4.6%
Other values (11)2242
31.0%
ValueCountFrequency (%)
21.511
 
0.2%
21.6194
 
2.7%
21.7293
4.1%
21.8725
10.0%
21.9596
8.3%
22572
7.9%
22.1330
4.6%
22.2473
6.5%
22.3278
 
3.8%
22.4309
4.3%
ValueCountFrequency (%)
23.513
 
0.2%
23.492
 
1.3%
23.3199
 
2.8%
23.2308
4.3%
23.1559
7.7%
23375
5.2%
22.9379
5.2%
22.8457
6.3%
22.7514
7.1%
22.6223
 
3.1%

T12
Real number (ℝ≥0)

HIGH CORRELATION

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.34311825
Minimum21.6
Maximum23.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.645697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum21.6
5-th percentile21.8
Q122
median22.3
Q322.7
95-th percentile23
Maximum23.2
Range1.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.4017536991
Coefficient of variation (CV)0.01798109354
Kurtosis-1.065930326
Mean22.34311825
Median Absolute Deviation (MAD)0.3
Skewness0.311254751
Sum161362
Variance0.1614060348
MonotonicityNot monotonic
2022-11-11T11:27:20.690602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
22.1762
10.6%
22.3730
10.1%
22721
10.0%
22.9708
9.8%
21.9697
9.7%
22.2688
9.5%
22.7528
7.3%
21.8497
6.9%
22.6447
 
6.2%
22.8321
 
4.4%
Other values (7)1123
15.5%
ValueCountFrequency (%)
21.684
 
1.2%
21.780
 
1.1%
21.8497
6.9%
21.9697
9.7%
22721
10.0%
22.1762
10.6%
22.2688
9.5%
22.3730
10.1%
22.4270
 
3.7%
22.5181
 
2.5%
ValueCountFrequency (%)
23.293
 
1.3%
23.1150
 
2.1%
23265
 
3.7%
22.9708
9.8%
22.8321
4.4%
22.7528
7.3%
22.6447
6.2%
22.5181
 
2.5%
22.4270
 
3.7%
22.3730
10.1%

Z
Real number (ℝ≥0)

HIGH CORRELATION

Distinct264
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.33072971
Minimum0
Maximum40.33
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.5 KiB
2022-11-11T11:27:20.749527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.03
Q18.13
median13.83
Q321.63
95-th percentile36.83
Maximum40.33
Range40.33
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.8949199
Coefficient of variation (CV)0.6454304579
Kurtosis-0.282125113
Mean15.33072971
Median Absolute Deviation (MAD)6.4
Skewness0.619420479
Sum110718.53
Variance97.90943984
MonotonicityNot monotonic
2022-11-11T11:27:20.809169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.13521
 
7.2%
13.83489
 
6.8%
8.53336
 
4.7%
3.63278
 
3.8%
7.03276
 
3.8%
27.23222
 
3.1%
1.63210
 
2.9%
28.13209
 
2.9%
2.53202
 
2.8%
20.13192
 
2.7%
Other values (254)4287
59.4%
ValueCountFrequency (%)
01
 
< 0.1%
0.0328
 
0.4%
0.831
 
< 0.1%
1.5342
 
0.6%
1.632
 
< 0.1%
1.63210
2.9%
2.0395
1.3%
2.431
 
< 0.1%
2.431
 
< 0.1%
2.53202
2.8%
ValueCountFrequency (%)
40.3315
 
0.2%
39.63167
2.3%
39.532
 
< 0.1%
39.5366
 
0.9%
39.031
 
< 0.1%
38.938
 
0.1%
38.731
 
< 0.1%
38.734
 
0.1%
38.6324
 
0.3%
38.4310
 
0.1%

Interactions

2022-11-11T11:27:18.069866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.519151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.277861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.152109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.978511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.818491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.623932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.405315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.295888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.105104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.851768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.710412image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.555213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.369584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.243683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.217222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.568983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.359546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.200944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.029340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.869319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.671860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.531889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.344723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.152942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.903594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.761189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.605437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.420413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.294512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.272207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.622367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.413417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.252829image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.083204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.921144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.723338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.586776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.395552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.203771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.958373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.814483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.658259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.475128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.349791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.326026image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.672367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.524642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.302897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.134655image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.969819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.771802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.638849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.444387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.252786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.009745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.865229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.710084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.526954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.402613image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.409799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.725189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.578461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.355768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.189626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.020648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.823674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.692474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.495216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.304632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.064224image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.919167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.763632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.581769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.459421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.461860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.771085image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.626370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.402936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.239458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.065519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.871286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.763909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.542058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.350677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.177715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.967006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.811716image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.630981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.513240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.518291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.819583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.676767image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.451935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.290287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.176146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.921118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.814738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.589566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.397554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.228544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.017888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.861701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.681261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.567058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.574028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.871376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.730261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.504723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.346077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.228601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.976930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.870599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.640394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.449831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.282362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.071169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.949371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.736149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.624863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.624856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.918251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.780534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.552472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.403883image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.275384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.029752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.920824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.687237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.496673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.332980image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.121408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.999204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.862681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.675692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.674710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:06.966275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.829935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.601181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.459694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.321229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.080581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.971614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.735075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.543562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.384897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.170311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.048039image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.914506image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.726521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.728529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.019102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.883886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.652742image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.520489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.373055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.135396image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.025433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.850694image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.594923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.439156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.223776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.102523image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.970318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.787620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.783344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.070976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.936745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.704648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.579297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.422236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.185894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.078898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.900603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.644755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.492786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.275898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.155079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.024137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.845682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.836166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.121940image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.989255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.821689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.641088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.471514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.236771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.132475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.950711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.695641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.547602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.328868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.206904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.077955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.901494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.889985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.174762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.044076image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.874511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.699890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.522658image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.292190image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.188251image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.002728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.748924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.602774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.383880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.261091image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.134764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.958243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.946793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:07.227654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.098891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:08.927388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:09.760685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:10.573946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:11.350500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:12.242069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.054571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:13.800847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:14.657589image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:15.502844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:16.315766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:17.189865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-11T11:27:18.015051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-11T11:27:20.868841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-11T11:27:20.948227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-11T11:27:21.028692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-11T11:27:21.108144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-11T11:27:21.188315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-11T11:27:19.045461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-11T11:27:19.166152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
00.000000022.322.322.322.322.422.922.322.323.122.021.921.60.00
10.083333022.322.322.322.322.422.922.322.323.122.021.921.60.03
20.166667022.322.322.322.322.422.922.322.323.122.021.921.60.03
30.250000022.322.322.322.322.422.922.322.323.122.021.921.60.03
40.333333022.322.322.322.322.422.922.322.323.122.021.921.60.03
50.416667022.322.322.322.322.422.922.322.323.122.021.921.60.03
60.500000022.322.322.322.322.422.922.322.323.122.021.821.60.03
70.583333022.322.322.322.322.422.922.322.323.122.021.921.60.03
80.666667022.322.322.322.322.422.922.322.323.122.021.821.60.03
90.750000022.322.322.322.322.422.922.322.323.122.021.821.60.03

Last rows

TIMEST1T2T3T4T5T6T7T8T9T10T11T12Z
7212601.000000022.923.023.023.023.123.023.022.924.522.322.421.92.93
7213601.083333022.923.023.023.023.123.023.022.924.522.322.321.92.93
7214601.166667022.923.023.023.023.123.023.022.924.522.322.421.92.93
7215601.250000022.923.023.023.023.123.023.022.924.522.322.421.92.93
7216601.333333022.923.023.023.023.123.023.022.924.522.322.421.92.93
7217601.416667022.923.023.023.023.123.023.022.924.522.322.421.92.93
7218601.500000022.923.023.023.023.123.023.022.924.522.322.421.92.93
7219601.583333022.923.023.023.023.123.023.022.924.522.322.421.92.93
7220601.666667022.923.023.023.023.123.023.022.924.522.322.321.92.93
7221601.750000022.923.023.023.023.123.023.022.924.522.322.321.92.93